Overview

Dataset statistics

Number of variables28
Number of observations356344
Missing cells2473043
Missing cells (%)24.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.1 MiB
Average record size in memory224.0 B

Variable types

Numeric6
Categorical22

Alerts

都道府県名 has constant value "東京都" Constant
市区町村名 has a high cardinality: 59 distinct values High cardinality
地区名 has a high cardinality: 1454 distinct values High cardinality
最寄駅：名称 has a high cardinality: 655 distinct values High cardinality
面積（㎡） has a high cardinality: 157 distinct values High cardinality
間口 has a high cardinality: 472 distinct values High cardinality
延床面積（㎡） has a high cardinality: 130 distinct values High cardinality
建築年 has a high cardinality: 75 distinct values High cardinality
用途 has a high cardinality: 202 distinct values High cardinality
id is highly correlated with 市区町村コードHigh correlation
市区町村コード is highly correlated with id and 1 other fieldsHigh correlation
建ぺい率（％） is highly correlated with 容積率（％）High correlation
容積率（％） is highly correlated with 市区町村コード and 1 other fieldsHigh correlation
id is highly correlated with 市区町村コードHigh correlation
市区町村コード is highly correlated with idHigh correlation
建ぺい率（％） is highly correlated with 容積率（％）High correlation
容積率（％） is highly correlated with 建ぺい率（％）High correlation
id is highly correlated with 市区町村コードHigh correlation
市区町村コード is highly correlated with idHigh correlation
建ぺい率（％） is highly correlated with 容積率（％）High correlation
容積率（％） is highly correlated with 建ぺい率（％）High correlation
id is highly correlated with 市区町村コード and 2 other fieldsHigh correlation
種類 is highly correlated with 建築年 and 5 other fieldsHigh correlation
地域 is highly correlated with 市区町村名 and 4 other fieldsHigh correlation
市区町村コード is highly correlated with id and 5 other fieldsHigh correlation
市区町村名 is highly correlated with id and 7 other fieldsHigh correlation
最寄駅：距離（分） is highly correlated with 市区町村コード and 3 other fieldsHigh correlation
間取り is highly correlated with 今後の利用目的High correlation
建築年 is highly correlated with 種類 and 2 other fieldsHigh correlation
建物の構造 is highly correlated with 種類 and 6 other fieldsHigh correlation
今後の利用目的 is highly correlated with 間取りHigh correlation
前面道路：種類 is highly correlated with 市区町村コード and 3 other fieldsHigh correlation
前面道路：幅員（ｍ） is highly correlated with 前面道路：種類High correlation
都市計画 is highly correlated with 種類 and 7 other fieldsHigh correlation
建ぺい率（％） is highly correlated with 種類 and 8 other fieldsHigh correlation
容積率（％） is highly correlated with 種類 and 5 other fieldsHigh correlation
取引時点 is highly correlated with id and 1 other fieldsHigh correlation
取引の事情等 is highly correlated with 種類 and 1 other fieldsHigh correlation
地域 has 159406 (44.7%) missing values Missing
最寄駅：距離（分） has 10129 (2.8%) missing values Missing
間取り has 202568 (56.8%) missing values Missing
土地の形状 has 159801 (44.8%) missing values Missing
間口 has 176081 (49.4%) missing values Missing
延床面積（㎡） has 236201 (66.3%) missing values Missing
建築年 has 82841 (23.2%) missing values Missing
建物の構造 has 78829 (22.1%) missing values Missing
用途 has 81061 (22.7%) missing values Missing
今後の利用目的 has 244965 (68.7%) missing values Missing
前面道路：方位 has 159830 (44.9%) missing values Missing
前面道路：種類 has 161971 (45.5%) missing values Missing
前面道路：幅員（ｍ） has 163065 (45.8%) missing values Missing
建ぺい率（％） has 5386 (1.5%) missing values Missing
容積率（％） has 5386 (1.5%) missing values Missing
改装 has 212071 (59.5%) missing values Missing
取引の事情等 has 328208 (92.1%) missing values Missing
y is highly skewed (γ1 = 63.8829345) Skewed
id is uniformly distributed Uniform
id has unique values Unique

Reproduction

Analysis started2022-03-24 05:27:26.480626
Analysis finished2022-03-24 05:28:17.545167
Duration51.06 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct356344
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178172.5
Minimum1
Maximum356344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-03-24T14:28:17.690976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17818.15
Q189086.75
median178172.5
Q3267258.25
95-th percentile338526.85
Maximum356344
Range356343
Interquartile range (IQR)178171.5

Descriptive statistics

Standard deviation102867.7965
Coefficient of variation (CV)0.5773494591
Kurtosis-1.2
Mean178172.5
Median Absolute Deviation (MAD)89086
Skewness8.870235055 × 10-16
Sum6.349070134 × 1010
Variance1.058178356 × 1010
MonotonicityStrictly increasing
2022-03-24T14:28:18.639919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
2375601
 
< 0.1%
2375681
 
< 0.1%
2375671
 
< 0.1%
2375661
 
< 0.1%
2375651
 
< 0.1%
2375641
 
< 0.1%
2375631
 
< 0.1%
2375621
 
< 0.1%
2375611
 
< 0.1%
Other values (356334)356334
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
3563441
< 0.1%
3563431
< 0.1%
3563421
< 0.1%
3563411
< 0.1%
3563401
< 0.1%
3563391
< 0.1%
3563381
< 0.1%
3563371
< 0.1%
3563361
< 0.1%
3563351
< 0.1%

種類
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
中古マンション等
158943 
宅地(土地と建物)
125344 
宅地(土地)
71594 
林地
 
381
農地
 
82

Length

Max length9
Median length8
Mean length7.942128954
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row中古マンション等
2nd row中古マンション等
3rd row中古マンション等
4th row中古マンション等
5th row宅地(土地と建物)

Common Values

ValueCountFrequency (%)
中古マンション等158943
44.6%
宅地(土地と建物)125344
35.2%
宅地(土地)71594
20.1%
林地381
 
0.1%
農地82
 
< 0.1%

Length

2022-03-24T14:28:18.776576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:28:18.851134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
中古マンション等158943
44.6%
宅地(土地と建物125344
35.2%
宅地(土地71594
20.1%
林地381
 
0.1%
農地82
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

地域
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing159406
Missing (%)44.7%
Memory size2.7 MiB
住宅地
174763 
商業地
20531 
工業地
 
1202
宅地見込地
 
442

Length

Max length5
Median length3
Mean length3.004488722
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row商業地
2nd row商業地
3rd row商業地
4th row商業地
5th row商業地

Common Values

ValueCountFrequency (%)
住宅地174763
49.0%
商業地20531
 
5.8%
工業地1202
 
0.3%
宅地見込地442
 
0.1%
(Missing)159406
44.7%

Length

2022-03-24T14:28:18.963667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:28:19.047728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
住宅地174763
88.7%
商業地20531
 
10.4%
工業地1202
 
0.6%
宅地見込地442
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

市区町村コード
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13140.01905
Minimum13101
Maximum13421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-03-24T14:28:19.141048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13101
5-th percentile13103
Q113110
median13117
Q313201
95-th percentile13222
Maximum13421
Range320
Interquartile range (IQR)91

Descriptive statistics

Standard deviation46.20604687
Coefficient of variation (CV)0.003516436824
Kurtosis0.8805375279
Mean13140.01905
Median Absolute Deviation (MAD)8
Skewness1.292414846
Sum4682366949
Variance2134.998767
MonotonicityNot monotonic
2022-03-24T14:28:19.450539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1311223288
 
6.5%
1311119705
 
5.5%
1312019655
 
5.5%
1312116309
 
4.6%
1311515654
 
4.4%
1311913973
 
3.9%
1320113212
 
3.7%
1312312670
 
3.6%
1310812540
 
3.5%
1310412121
 
3.4%
Other values (49)197217
55.3%
ValueCountFrequency (%)
131013673
 
1.0%
131028421
2.4%
1310311001
3.1%
1310412121
3.4%
131058498
2.4%
131066828
1.9%
131077909
2.2%
1310812540
3.5%
1310911771
3.3%
131107191
2.0%
ValueCountFrequency (%)
1342152
 
< 0.1%
13401233
 
0.1%
1338172
 
< 0.1%
1336418
 
< 0.1%
1336326
 
< 0.1%
13361295
 
0.1%
13308141
 
< 0.1%
1330747
 
< 0.1%
13305449
0.1%
13303796
0.2%

都道府県名
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
東京都
356344 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row東京都
2nd row東京都
3rd row東京都
4th row東京都
5th row東京都

Common Values

ValueCountFrequency (%)
東京都356344
100.0%

Length

2022-03-24T14:28:19.582710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:28:19.644964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
東京都356344
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

市区町村名
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
世田谷区
 
23288
大田区
 
19705
練馬区
 
19655
足立区
 
16309
杉並区
 
15654
Other values (54)
261733 

Length

Max length8
Median length3
Mean length3.201238129
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row千代田区
2nd row千代田区
3rd row千代田区
4th row千代田区
5th row千代田区

Common Values

ValueCountFrequency (%)
世田谷区23288
 
6.5%
大田区19705
 
5.5%
練馬区19655
 
5.5%
足立区16309
 
4.6%
杉並区15654
 
4.4%
板橋区13973
 
3.9%
八王子市13212
 
3.7%
江戸川区12670
 
3.6%
江東区12540
 
3.5%
新宿区12121
 
3.4%
Other values (49)197217
55.3%

Length

2022-03-24T14:28:19.709189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
世田谷区23288
 
6.5%
大田区19705
 
5.5%
練馬区19655
 
5.5%
足立区16309
 
4.6%
杉並区15654
 
4.4%
板橋区13973
 
3.9%
八王子市13212
 
3.7%
江戸川区12670
 
3.6%
江東区12540
 
3.5%
新宿区12121
 
3.4%
Other values (49)197217
55.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

地区名
Categorical

HIGH CARDINALITY

Distinct1454
Distinct (%)0.4%
Missing246
Missing (%)0.1%
Memory size2.7 MiB
本町
 
3199
中央
 
2108
亀戸
 
1849
中町
 
1611
栄町
 
1493
Other values (1449)
345838 

Length

Max length9
Median length3
Mean length2.726990885
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st row飯田橋
2nd row飯田橋
3rd row飯田橋
4th row飯田橋
5th row飯田橋

Common Values

ValueCountFrequency (%)
本町3199
 
0.9%
中央2108
 
0.6%
亀戸1849
 
0.5%
中町1611
 
0.5%
栄町1493
 
0.4%
新町1386
 
0.4%
新宿1372
 
0.4%
大島1296
 
0.4%
西新宿1248
 
0.4%
南大井1221
 
0.3%
Other values (1444)339315
95.2%

Length

2022-03-24T14:28:19.824320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
本町3199
 
0.9%
中央2108
 
0.6%
亀戸1849
 
0.5%
中町1611
 
0.5%
栄町1493
 
0.4%
新町1386
 
0.4%
新宿1372
 
0.4%
大島1296
 
0.4%
西新宿1248
 
0.4%
南大井1221
 
0.3%
Other values (1444)339315
95.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

最寄駅：名称
Categorical

HIGH CARDINALITY

Distinct655
Distinct (%)0.2%
Missing1551
Missing (%)0.4%
Memory size2.7 MiB
八王子
 
3315
大泉学園
 
3020
新小岩
 
2927
金町
 
2306
三鷹
 
2182
Other values (650)
341043 

Length

Max length13
Median length3
Mean length3.474034155
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row飯田橋
2nd row飯田橋
3rd row飯田橋
4th row飯田橋
5th row飯田橋

Common Values

ValueCountFrequency (%)
八王子3315
 
0.9%
大泉学園3020
 
0.8%
新小岩2927
 
0.8%
金町2306
 
0.6%
三鷹2182
 
0.6%
小岩2142
 
0.6%
荻窪2103
 
0.6%
武蔵小金井2062
 
0.6%
大森(東京)2022
 
0.6%
竹ノ塚2015
 
0.6%
Other values (645)330699
92.8%

Length

2022-03-24T14:28:19.942508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
八王子3315
 
0.9%
大泉学園3020
 
0.9%
新小岩2927
 
0.8%
金町2306
 
0.6%
三鷹2182
 
0.6%
小岩2142
 
0.6%
荻窪2103
 
0.6%
武蔵小金井2062
 
0.6%
大森(東京2022
 
0.6%
竹ノ塚2015
 
0.6%
Other values (645)330699
93.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

最寄駅：距離（分）
Categorical

HIGH CORRELATION
MISSING

Distinct34
Distinct (%)< 0.1%
Missing10129
Missing (%)2.8%
Memory size2.7 MiB
6
29225 
8
26268 
4
26231 
5
25048 
9
23869 
Other values (29)
215574 

Length

Max length7
Median length1
Mean length1.592181159
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row5
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
629225
 
8.2%
826268
 
7.4%
426231
 
7.4%
525048
 
7.0%
923869
 
6.7%
722012
 
6.2%
321696
 
6.1%
1118947
 
5.3%
1018463
 
5.2%
214428
 
4.0%
Other values (24)120028
33.7%

Length

2022-03-24T14:28:20.057008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
629225
 
8.4%
826268
 
7.6%
426231
 
7.6%
525048
 
7.2%
923869
 
6.9%
722012
 
6.4%
321696
 
6.3%
1118947
 
5.5%
1018463
 
5.3%
214428
 
4.2%
Other values (24)120028
34.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

間取り
Categorical

HIGH CORRELATION
MISSING

Distinct48
Distinct (%)< 0.1%
Missing202568
Missing (%)56.8%
Memory size2.7 MiB
３ＬＤＫ
43231 
１Ｋ
42074 
２ＬＤＫ
26925 
１ＬＤＫ
11956 
１ＤＫ
8281 
Other values (43)
21309 

Length

Max length7
Median length4
Mean length3.33888253
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st row２ＬＤＫ
2nd row１Ｋ
3rd row１ＬＤＫ
4th row１Ｒ
5th row２ＬＤＫ

Common Values

ValueCountFrequency (%)
３ＬＤＫ43231
 
12.1%
１Ｋ42074
 
11.8%
２ＬＤＫ26925
 
7.6%
１ＬＤＫ11956
 
3.4%
１ＤＫ8281
 
2.3%
２ＤＫ7140
 
2.0%
４ＬＤＫ4638
 
1.3%
１Ｒ2961
 
0.8%
３ＤＫ2491
 
0.7%
オープンフロア1467
 
0.4%
Other values (38)2612
 
0.7%
(Missing)202568
56.8%

Length

2022-03-24T14:28:20.165110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
３ｌｄｋ43231
28.1%
１ｋ42074
27.4%
２ｌｄｋ26925
17.5%
１ｌｄｋ11956
 
7.8%
１ｄｋ8281
 
5.4%
２ｄｋ7140
 
4.6%
４ｌｄｋ4638
 
3.0%
１ｒ2961
 
1.9%
３ｄｋ2491
 
1.6%
オープンフロア1467
 
1.0%
Other values (38)2612
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

面積（㎡）
Categorical

HIGH CARDINALITY

Distinct157
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
20
25395 
65
 
19968
70
 
19695
60
 
19218
55
 
18439
Other values (152)
253629 

Length

Max length7
Median length2
Mean length2.378996139
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row55
2nd row20
3rd row45
4th row20
5th row80

Common Values

ValueCountFrequency (%)
2025395
 
7.1%
6519968
 
5.6%
7019695
 
5.5%
6019218
 
5.4%
5518439
 
5.2%
5015954
 
4.5%
7514172
 
4.0%
1512590
 
3.5%
10012530
 
3.5%
8011590
 
3.3%
Other values (147)186793
52.4%

Length

2022-03-24T14:28:20.267089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2025395
 
7.1%
6519968
 
5.6%
7019695
 
5.5%
6019218
 
5.4%
5518439
 
5.2%
5015954
 
4.5%
7514172
 
4.0%
1512590
 
3.5%
10012530
 
3.5%
8011590
 
3.3%
Other values (147)186793
52.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

土地の形状
Categorical

MISSING

Distinct9
Distinct (%)< 0.1%
Missing159801
Missing (%)44.8%
Memory size2.7 MiB
ほぼ長方形
68664 
長方形
47894 
不整形
31165 
ほぼ台形
15246 
ほぼ正方形
10355 
Other values (4)
23219 

Length

Max length5
Median length4
Mean length3.88352676
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowほぼ台形
2nd rowほぼ長方形
3rd rowほぼ長方形
4th rowほぼ長方形
5th rowほぼ正方形

Common Values

ValueCountFrequency (%)
ほぼ長方形68664
19.3%
長方形47894
 
13.4%
不整形31165
 
8.7%
ほぼ台形15246
 
4.3%
ほぼ正方形10355
 
2.9%
ほぼ整形7732
 
2.2%
台形7365
 
2.1%
袋地等6794
 
1.9%
正方形1328
 
0.4%
(Missing)159801
44.8%

Length

2022-03-24T14:28:20.368549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:28:20.447128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ほぼ長方形68664
34.9%
長方形47894
24.4%
不整形31165
15.9%
ほぼ台形15246
 
7.8%
ほぼ正方形10355
 
5.3%
ほぼ整形7732
 
3.9%
台形7365
 
3.7%
袋地等6794
 
3.5%
正方形1328
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

間口
Categorical

HIGH CARDINALITY
MISSING

Distinct472
Distinct (%)0.3%
Missing176081
Missing (%)49.4%
Memory size2.7 MiB
8.0
 
8136
10.0
 
8024
7.0
 
7579
9.0
 
7397
6.0
 
7285
Other values (467)
141842 

Length

Max length7
Median length3
Mean length3.378141937
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st row6.8
2nd row13.0
3rd row6.8
4th row6.4
5th row9.4

Common Values

ValueCountFrequency (%)
8.08136
 
2.3%
10.08024
 
2.3%
7.07579
 
2.1%
9.07397
 
2.1%
6.07285
 
2.0%
5.06003
 
1.7%
11.05066
 
1.4%
12.05066
 
1.4%
2.54591
 
1.3%
7.53842
 
1.1%
Other values (462)117274
32.9%
(Missing)176081
49.4%

Length

2022-03-24T14:28:20.563264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8.08136
 
4.5%
10.08024
 
4.5%
7.07579
 
4.2%
9.07397
 
4.1%
6.07285
 
4.0%
5.06003
 
3.3%
11.05066
 
2.8%
12.05066
 
2.8%
2.54591
 
2.5%
7.53842
 
2.1%
Other values (462)117274
65.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

延床面積（㎡）
Categorical

HIGH CARDINALITY
MISSING

Distinct130
Distinct (%)0.1%
Missing236201
Missing (%)66.3%
Memory size2.7 MiB
95
15094 
100
12621 
90
12555 
85
9468 
80
7601 
Other values (125)
62804 

Length

Max length7
Median length2
Mean length2.530143246
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row330
2nd row460
3rd row250
4th row500
5th row1200

Common Values

ValueCountFrequency (%)
9515094
 
4.2%
10012621
 
3.5%
9012555
 
3.5%
859468
 
2.7%
807601
 
2.1%
1056526
 
1.8%
754698
 
1.3%
703631
 
1.0%
1103520
 
1.0%
1152654
 
0.7%
Other values (120)41775
 
11.7%
(Missing)236201
66.3%

Length

2022-03-24T14:28:20.671457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9515094
 
12.6%
10012621
 
10.5%
9012555
 
10.5%
859468
 
7.9%
807601
 
6.3%
1056526
 
5.4%
754698
 
3.9%
703631
 
3.0%
1103520
 
2.9%
1152654
 
2.2%
Other values (120)41775
34.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

建築年
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct75
Distinct (%)< 0.1%
Missing82841
Missing (%)23.2%
Memory size2.7 MiB
平成19年
 
11719
平成18年
 
11179
平成20年
 
10746
平成17年
 
10598
平成21年
 
9419
Other values (70)
219842 

Length

Max length5
Median length5
Mean length4.862334234
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row昭和59年
2nd row平成15年
3rd row平成24年
4th row平成15年
5th row昭和61年

Common Values

ValueCountFrequency (%)
平成19年11719
 
3.3%
平成18年11179
 
3.1%
平成20年10746
 
3.0%
平成17年10598
 
3.0%
平成21年9419
 
2.6%
平成22年8278
 
2.3%
平成24年8277
 
2.3%
平成23年7906
 
2.2%
平成15年7848
 
2.2%
平成16年7776
 
2.2%
Other values (65)179757
50.4%
(Missing)82841
23.2%

Length

2022-03-24T14:28:20.770978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
平成19年11719
 
4.3%
平成18年11179
 
4.1%
平成20年10746
 
3.9%
平成17年10598
 
3.9%
平成21年9419
 
3.4%
平成22年8278
 
3.0%
平成24年8277
 
3.0%
平成23年7906
 
2.9%
平成15年7848
 
2.9%
平成16年7776
 
2.8%
Other values (65)179757
65.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

建物の構造
Categorical

HIGH CORRELATION
MISSING

Distinct24
Distinct (%)< 0.1%
Missing78829
Missing (%)22.1%
Memory size2.7 MiB
ＲＣ
115324 
木造
95139 
ＳＲＣ
51926 
鉄骨造
 
11199
軽量鉄骨造
 
2695
Other values (19)
 
1232

Length

Max length15
Median length2
Mean length2.272327622
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowＳＲＣ
2nd rowＲＣ
3rd rowＲＣ
4th rowＲＣ
5th rowＲＣ

Common Values

ValueCountFrequency (%)
ＲＣ115324
32.4%
木造95139
26.7%
ＳＲＣ51926
14.6%
鉄骨造11199
 
3.1%
軽量鉄骨造2695
 
0.8%
ＲＣ、木造661
 
0.2%
鉄骨造、木造170
 
< 0.1%
ＲＣ、鉄骨造117
 
< 0.1%
ＳＲＣ、ＲＣ110
 
< 0.1%
ブロック造95
 
< 0.1%
Other values (14)79
 
< 0.1%
(Missing)78829
22.1%

Length

2022-03-24T14:28:20.874711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ｒｃ115324
41.6%
木造95139
34.3%
ｓｒｃ51926
18.7%
鉄骨造11199
 
4.0%
軽量鉄骨造2695
 
1.0%
ｒｃ、木造661
 
0.2%
鉄骨造、木造170
 
0.1%
ｒｃ、鉄骨造117
 
< 0.1%
ｓｒｃ、ｒｃ110
 
< 0.1%
ブロック造95
 
< 0.1%
Other values (14)79
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

用途
Categorical

HIGH CARDINALITY
MISSING

Distinct202
Distinct (%)0.1%
Missing81061
Missing (%)22.7%
Memory size2.7 MiB
住宅
245908 
共同住宅
 
9991
事務所
 
2480
住宅、店舗
 
2433
店舗
 
1674
Other values (197)
 
12797

Length

Max length21
Median length2
Mean length2.341088988
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)< 0.1%

Sample

1st row住宅
2nd row住宅
3rd row住宅、事務所、店舗
4th row住宅
5th row住宅

Common Values

ValueCountFrequency (%)
住宅245908
69.0%
共同住宅9991
 
2.8%
事務所2480
 
0.7%
住宅、店舗2433
 
0.7%
店舗1674
 
0.5%
共同住宅、店舗1446
 
0.4%
事務所、店舗1141
 
0.3%
住宅、事務所871
 
0.2%
その他739
 
0.2%
共同住宅、事務所638
 
0.2%
Other values (192)7962
 
2.2%
(Missing)81061
 
22.7%

Length

2022-03-24T14:28:20.993185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
住宅245908
89.3%
共同住宅9991
 
3.6%
事務所2480
 
0.9%
住宅、店舗2433
 
0.9%
店舗1674
 
0.6%
共同住宅、店舗1446
 
0.5%
事務所、店舗1141
 
0.4%
住宅、事務所871
 
0.3%
その他739
 
0.3%
共同住宅、事務所638
 
0.2%
Other values (192)7962
 
2.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

今後の利用目的
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing244965
Missing (%)68.7%
Memory size2.7 MiB
住宅
97455 
その他
 
9651
事務所
 
2306
店舗
 
1382
倉庫
 
378

Length

Max length3
Median length2
Mean length2.107354169
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row住宅
2nd row住宅
3rd rowその他
4th row住宅
5th row事務所

Common Values

ValueCountFrequency (%)
住宅97455
 
27.3%
その他9651
 
2.7%
事務所2306
 
0.6%
店舗1382
 
0.4%
倉庫378
 
0.1%
工場207
 
0.1%
(Missing)244965
68.7%

Length

2022-03-24T14:28:21.102665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:28:21.172025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
住宅97455
87.5%
その他9651
 
8.7%
事務所2306
 
2.1%
店舗1382
 
1.2%
倉庫378
 
0.3%
工場207
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

前面道路：方位
Categorical

MISSING

Distinct9
Distinct (%)< 0.1%
Missing159830
Missing (%)44.9%
Memory size2.7 MiB
南
31046 
北
29371 
東
28546 
西
28215 
南東
20005 
Other values (4)
59331 

Length

Max length5
Median length1
Mean length1.436401478
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row南西
2nd row北東
3rd row北東
4th row南西
5th row北東

Common Values

ValueCountFrequency (%)
南31046
 
8.7%
北29371
 
8.2%
東28546
 
8.0%
西28215
 
7.9%
南東20005
 
5.6%
南西19521
 
5.5%
北西19460
 
5.5%
北東18209
 
5.1%
接面道路無2141
 
0.6%
(Missing)159830
44.9%

Length

2022-03-24T14:28:21.273902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:28:21.360894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
南31046
15.8%
北29371
14.9%
東28546
14.5%
西28215
14.4%
南東20005
10.2%
南西19521
9.9%
北西19460
9.9%
北東18209
9.3%
接面道路無2141
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

前面道路：種類
Categorical

HIGH CORRELATION
MISSING

Distinct14
Distinct (%)< 0.1%
Missing161971
Missing (%)45.5%
Memory size2.7 MiB
区道
82322 
私道
48210 
市道
45598 
都道
8679 
道路
 
5087
Other values (9)
 
4477

Length

Max length4
Median length2
Mean length2.007696542
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row区道
2nd row区道
3rd row区道
4th row都道
5th row区道

Common Values

ValueCountFrequency (%)
区道82322
23.1%
私道48210
 
13.5%
市道45598
 
12.8%
都道8679
 
2.4%
道路5087
 
1.4%
国道1690
 
0.5%
町道1364
 
0.4%
区画街路748
 
0.2%
村道304
 
0.1%
農道115
 
< 0.1%
Other values (4)256
 
0.1%
(Missing)161971
45.5%

Length

2022-03-24T14:28:21.472429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
区道82322
42.4%
私道48210
24.8%
市道45598
23.5%
都道8679
 
4.5%
道路5087
 
2.6%
国道1690
 
0.9%
町道1364
 
0.7%
区画街路748
 
0.4%
村道304
 
0.2%
農道115
 
0.1%
Other values (4)256
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

前面道路：幅員（ｍ）
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct371
Distinct (%)0.2%
Missing163065
Missing (%)45.8%
Infinite0
Infinite (%)0.0%
Mean6.257812282
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-03-24T14:28:21.601205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q36
95-th percentile15
Maximum90
Range89
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.902257685
Coefficient of variation (CV)0.7833820294
Kurtosis22.2490835
Mean6.257812282
Median Absolute Deviation (MAD)1
Skewness4.064124196
Sum1209503.7
Variance24.03213041
MonotonicityNot monotonic
2022-03-24T14:28:21.728700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444001
 
12.3%
628239
 
7.9%
518755
 
5.3%
4.510427
 
2.9%
86231
 
1.7%
5.44140
 
1.2%
3.64087
 
1.1%
5.53987
 
1.1%
33337
 
0.9%
113288
 
0.9%
Other values (361)66787
18.7%
(Missing)163065
45.8%
ValueCountFrequency (%)
1100
 
< 0.1%
1.18
 
< 0.1%
1.259
 
< 0.1%
1.330
 
< 0.1%
1.433
 
< 0.1%
1.5153
 
< 0.1%
1.677
 
< 0.1%
1.779
 
< 0.1%
1.81118
0.3%
1.9126
 
< 0.1%
ValueCountFrequency (%)
901
< 0.1%
851
< 0.1%
802
< 0.1%
761
< 0.1%
751
< 0.1%
702
< 0.1%
661
< 0.1%
651
< 0.1%
642
< 0.1%
63.31
< 0.1%

都市計画
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing3447
Missing (%)1.0%
Memory size2.7 MiB
第１種低層住居専用地域
89510 
商業地域
61882 
第１種中高層住居専用地域
50881 
準工業地域
48779 
第１種住居地域
38718 
Other values (11)
63127 

Length

Max length22
Median length7
Mean length7.947695787
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row商業地域
2nd row商業地域
3rd row商業地域
4th row商業地域
5th row商業地域

Common Values

ValueCountFrequency (%)
第１種低層住居専用地域89510
25.1%
商業地域61882
17.4%
第１種中高層住居専用地域50881
14.3%
準工業地域48779
13.7%
第１種住居地域38718
10.9%
近隣商業地域33436
 
9.4%
第２種中高層住居専用地域8757
 
2.5%
第２種住居地域7218
 
2.0%
工業地域4794
 
1.3%
準住居地域4778
 
1.3%
Other values (6)4144
 
1.2%
(Missing)3447
 
1.0%

Length

2022-03-24T14:28:21.854673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
第１種低層住居専用地域89510
25.4%
商業地域61882
17.5%
第１種中高層住居専用地域50881
14.4%
準工業地域48779
13.8%
第１種住居地域38718
11.0%
近隣商業地域33436
 
9.5%
第２種中高層住居専用地域8757
 
2.5%
第２種住居地域7218
 
2.0%
工業地域4794
 
1.4%
準住居地域4778
 
1.4%
Other values (6)4144
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

建ぺい率（％）
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing5386
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean62.00548214
Minimum30
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-03-24T14:28:21.951084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile40
Q160
median60
Q380
95-th percentile80
Maximum80
Range50
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.12522084
Coefficient of variation (CV)0.2116783933
Kurtosis-0.8633023597
Mean62.00548214
Median Absolute Deviation (MAD)10
Skewness0.02831809409
Sum21761320
Variance172.271422
MonotonicityNot monotonic
2022-03-24T14:28:22.047858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
60166436
46.7%
8099496
27.9%
4042936
 
12.0%
5040689
 
11.4%
30862
 
0.2%
70539
 
0.2%
(Missing)5386
 
1.5%
ValueCountFrequency (%)
30862
 
0.2%
4042936
 
12.0%
5040689
 
11.4%
60166436
46.7%
70539
 
0.2%
8099496
27.9%
ValueCountFrequency (%)
8099496
27.9%
70539
 
0.2%
60166436
46.7%
5040689
 
11.4%
4042936
 
12.0%
30862
 
0.2%

容積率（％）
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct17
Distinct (%)< 0.1%
Missing5386
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean264.1063603
Minimum50
Maximum1300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-03-24T14:28:22.141667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile80
Q1150
median200
Q3300
95-th percentile600
Maximum1300
Range1250
Interquartile range (IQR)150

Descriptive statistics

Standard deviation159.4182523
Coefficient of variation (CV)0.6036138324
Kurtosis0.4531086916
Mean264.1063603
Median Absolute Deviation (MAD)100
Skewness0.9883428785
Sum92690240
Variance25414.17917
MonotonicityNot monotonic
2022-03-24T14:28:22.245873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
20099671
28.0%
30067243
18.9%
8043846
12.3%
40032376
 
9.1%
10032122
 
9.0%
50026878
 
7.5%
15022580
 
6.3%
60016993
 
4.8%
7006430
 
1.8%
8001496
 
0.4%
Other values (7)1323
 
0.4%
(Missing)5386
 
1.5%
ValueCountFrequency (%)
50264
 
0.1%
60931
 
0.3%
8043846
12.3%
10032122
 
9.0%
15022580
 
6.3%
20099671
28.0%
30067243
18.9%
40032376
 
9.1%
50026878
 
7.5%
60016993
 
4.8%
ValueCountFrequency (%)
13002
 
< 0.1%
12007
 
< 0.1%
11002
 
< 0.1%
100057
 
< 0.1%
90060
 
< 0.1%
8001496
 
0.4%
7006430
 
1.8%
60016993
4.8%
50026878
7.5%
40032376
9.1%

取引時点
Categorical

HIGH CORRELATION

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2013年第１四半期
 
8558
2013年第２四半期
 
8485
2015年第１四半期
 
8470
2013年第３四半期
 
8460
2016年第１四半期
 
8325
Other values (45)
314046 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017年第４四半期
2nd row2017年第４四半期
3rd row2017年第３四半期
4th row2017年第３四半期
5th row2017年第２四半期

Common Values

ValueCountFrequency (%)
2013年第１四半期8558
 
2.4%
2013年第２四半期8485
 
2.4%
2015年第１四半期8470
 
2.4%
2013年第３四半期8460
 
2.4%
2016年第１四半期8325
 
2.3%
2015年第３四半期8255
 
2.3%
2016年第３四半期8206
 
2.3%
2017年第１四半期8186
 
2.3%
2014年第１四半期8166
 
2.3%
2013年第４四半期8146
 
2.3%
Other values (40)273087
76.6%

Length

2022-03-24T14:28:22.353939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013年第１四半期8558
 
2.4%
2013年第２四半期8485
 
2.4%
2015年第１四半期8470
 
2.4%
2013年第３四半期8460
 
2.4%
2016年第１四半期8325
 
2.3%
2015年第３四半期8255
 
2.3%
2016年第３四半期8206
 
2.3%
2017年第１四半期8186
 
2.3%
2014年第１四半期8166
 
2.3%
2013年第４四半期8146
 
2.3%
Other values (40)273087
76.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

改装
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing212071
Missing (%)59.5%
Memory size2.7 MiB
未改装
106630 
改装済
37643 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row改装済
2nd row未改装
3rd row未改装
4th row未改装
5th row改装済

Common Values

ValueCountFrequency (%)
未改装106630
29.9%
改装済37643
 
10.6%
(Missing)212071
59.5%

Length

2022-03-24T14:28:22.658054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:28:22.723125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
未改装106630
73.9%
改装済37643
 
26.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

取引の事情等
Categorical

HIGH CORRELATION
MISSING

Distinct25
Distinct (%)0.1%
Missing328208
Missing (%)92.1%
Memory size2.7 MiB
私道を含む取引
18728 
調停・競売等
5458 
隣地の購入
 
1805
関係者間取引
 
1195
調停・競売等、私道を含む取引
 
424
Other values (20)
 
526

Length

Max length21
Median length7
Mean length6.826627808
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st row私道を含む取引
2nd row私道を含む取引
3rd row私道を含む取引
4th row隣地の購入
5th row関係者間取引

Common Values

ValueCountFrequency (%)
私道を含む取引18728
 
5.3%
調停・競売等5458
 
1.5%
隣地の購入1805
 
0.5%
関係者間取引1195
 
0.3%
調停・競売等、私道を含む取引424
 
0.1%
隣地の購入、私道を含む取引189
 
0.1%
その他事情有り104
 
< 0.1%
関係者間取引、私道を含む取引100
 
< 0.1%
隣地の購入、関係者間取引33
 
< 0.1%
他の権利・負担付き29
 
< 0.1%
Other values (15)71
 
< 0.1%
(Missing)328208
92.1%

Length

2022-03-24T14:28:22.802880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
私道を含む取引18728
66.6%
調停・競売等5458
 
19.4%
隣地の購入1805
 
6.4%
関係者間取引1195
 
4.2%
調停・競売等、私道を含む取引424
 
1.5%
隣地の購入、私道を含む取引189
 
0.7%
その他事情有り104
 
0.4%
関係者間取引、私道を含む取引100
 
0.4%
隣地の購入、関係者間取引33
 
0.1%
他の権利・負担付き29
 
0.1%
Other values (15)71
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

y
Real number (ℝ≥0)

SKEWED

Distinct493
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.43476559
Minimum0.0005
Maximum61000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-03-24T14:28:22.941011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile8
Q121
median35
Q353
95-th percentile160
Maximum61000
Range60999.9995
Interquartile range (IQR)32

Descriptive statistics

Standard deviation315.0113392
Coefficient of variation (CV)4.81412803
Kurtosis7603.439125
Mean65.43476559
Median Absolute Deviation (MAD)15
Skewness63.8829345
Sum23317286.11
Variance99232.14379
MonotonicityNot monotonic
2022-03-24T14:28:23.085085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
307802
 
2.2%
207798
 
2.2%
257656
 
2.1%
237344
 
2.1%
227282
 
2.0%
407277
 
2.0%
247188
 
2.0%
357044
 
2.0%
216828
 
1.9%
326547
 
1.8%
Other values (483)283578
79.6%
ValueCountFrequency (%)
0.00051
 
< 0.1%
0.0013
< 0.1%
0.00122
< 0.1%
0.00151
 
< 0.1%
0.00161
 
< 0.1%
0.0021
 
< 0.1%
0.00272
< 0.1%
0.0041
 
< 0.1%
0.0051
 
< 0.1%
0.013
< 0.1%
ValueCountFrequency (%)
610001
< 0.1%
450001
< 0.1%
380001
< 0.1%
320002
< 0.1%
270002
< 0.1%
250001
< 0.1%
240001
< 0.1%
230002
< 0.1%
220001
< 0.1%
210001
< 0.1%

Interactions

2022-03-24T14:28:04.010777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:27:58.288675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:27:59.392886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:00.428879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:01.423209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:02.736587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:04.222575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:27:58.479490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:27:59.567694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:00.609792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:01.606204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:03.027180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:04.400668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:27:58.638051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:27:59.716786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:00.786085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:01.782356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:03.236039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:04.583271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:27:58.814559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:27:59.911892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:00.927534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:01.962734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:03.428124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:04.770909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:27:59.016802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:00.090090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:01.074097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:02.143738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:03.618077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:04.954654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:27:59.213887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:00.275718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:01.230779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:02.384861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:28:03.816244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-24T14:28:23.202898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-24T14:28:23.335818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-24T14:28:23.461945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-24T14:28:23.615721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-24T14:28:06.243114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-24T14:28:08.642194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-24T14:28:14.935860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-24T14:28:16.651519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

id種類地域市区町村コード都道府県名市区町村名地区名最寄駅：名称最寄駅：距離（分）間取り面積（㎡）土地の形状間口延床面積（㎡）建築年建物の構造用途今後の利用目的前面道路：方位前面道路：種類前面道路：幅員（ｍ）都市計画建ぺい率（％）容積率（％）取引時点改装取引の事情等y
01中古マンション等NaN13101東京都千代田区飯田橋飯田橋1２ＬＤＫ55NaNNaNNaN昭和59年ＳＲＣNaN住宅NaNNaNNaN商業地域80.0600.02017年第４四半期改装済NaN66.0
12中古マンション等NaN13101東京都千代田区飯田橋飯田橋5１Ｋ20NaNNaNNaN平成15年ＲＣNaN住宅NaNNaNNaN商業地域80.0500.02017年第４四半期未改装NaN19.0
23中古マンション等NaN13101東京都千代田区飯田橋飯田橋3１ＬＤＫ45NaNNaNNaN平成24年ＲＣ住宅その他NaNNaNNaN商業地域80.0500.02017年第３四半期未改装NaN37.0
34中古マンション等NaN13101東京都千代田区飯田橋飯田橋5１Ｒ20NaNNaNNaN平成15年ＲＣ住宅住宅NaNNaNNaN商業地域80.0500.02017年第３四半期未改装NaN18.0
45宅地(土地と建物)商業地13101東京都千代田区飯田橋飯田橋3NaN80ほぼ台形6.8330昭和61年ＲＣ住宅、事務所、店舗事務所南西区道8.0商業地域80.0500.02017年第２四半期NaNNaN240.0
56中古マンション等NaN13101東京都千代田区飯田橋飯田橋1２ＬＤＫ55NaNNaNNaN昭和59年ＳＲＣ住宅住宅NaNNaNNaN商業地域80.0600.02017年第２四半期改装済NaN65.0
67中古マンション等NaN13101東京都千代田区飯田橋飯田橋4２ＬＤＫ55NaNNaNNaN平成11年ＳＲＣ住宅NaNNaNNaNNaN商業地域80.0500.02017年第２四半期未改装NaN53.0
78中古マンション等NaN13101東京都千代田区飯田橋飯田橋1NaN55NaNNaNNaN昭和59年ＳＲＣ住宅住宅NaNNaNNaN商業地域80.0600.02017年第２四半期未改装NaN65.0
89中古マンション等NaN13101東京都千代田区飯田橋飯田橋1２ＬＤＫ55NaNNaNNaN昭和59年ＳＲＣ住宅住宅NaNNaNNaN商業地域80.0600.02017年第２四半期未改装NaN56.0
910中古マンション等NaN13101東京都千代田区飯田橋飯田橋3１Ｋ20NaNNaNNaN昭和60年ＳＲＣ住宅住宅NaNNaNNaN商業地域80.0700.02017年第１四半期未改装NaN15.0

Last rows

id種類地域市区町村コード都道府県名市区町村名地区名最寄駅：名称最寄駅：距離（分）間取り面積（㎡）土地の形状間口延床面積（㎡）建築年建物の構造用途今後の利用目的前面道路：方位前面道路：種類前面道路：幅員（ｍ）都市計画建ぺい率（％）容積率（％）取引時点改装取引の事情等y
356334356335宅地(土地)住宅地13421東京都小笠原村父島NaNNaNNaN380ほぼ長方形15.0NaNNaNNaNNaNNaN東村道4.5市街化区域及び市街化調整区域外の都市計画区域70.0200.02008年第１四半期NaNNaN12.00
356335356336宅地(土地と建物)商業地13421東京都小笠原村父島NaNNaNNaN500不整形21.0360昭和64年木造その他NaN南東都道17.0市街化区域及び市街化調整区域外の都市計画区域70.0200.02007年第４四半期NaNNaN85.00
356336356337宅地(土地)住宅地13421東京都小笠原村父島NaNNaNNaN85ほぼ長方形6.6NaNNaNNaNNaNNaN接面道路無NaNNaN都市計画区域外70.0200.02007年第３四半期NaN隣地の購入3.90
356337356338宅地(土地)住宅地13421東京都小笠原村父島NaNNaNNaN105ほぼ長方形6.0NaNNaNNaNNaNNaN西村道6.0市街化区域及び市街化調整区域外の都市計画区域70.0200.02007年第２四半期NaNNaN10.00
356338356339林地NaN13421東京都小笠原村父島NaNNaNNaN710NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2007年第２四半期NaNNaN1.90
356339356340宅地(土地)宅地見込地13421東京都小笠原村父島NaNNaNNaN500ほぼ長方形NaNNaNNaNNaNNaNNaN接面道路無NaNNaN市街化区域及び市街化調整区域外の都市計画区域70.0200.02007年第１四半期NaNNaN2.30
356340356341宅地(土地)宅地見込地13421東京都小笠原村父島NaNNaNNaN115ほぼ整形10.0NaNNaNNaNNaNNaN西村道5.5市街化区域及び市街化調整区域外の都市計画区域70.0200.02007年第１四半期NaNNaN0.61
356341356342宅地(土地)住宅地13421東京都小笠原村母島NaNNaNNaN230ほぼ長方形15.0NaNNaNNaNNaNNaN北西村道4.8市街化区域及び市街化調整区域外の都市計画区域70.0200.02008年第３四半期NaN隣地の購入7.00
356342356343宅地(土地)宅地見込地13421東京都小笠原村母島NaNNaNNaN175ほぼ長方形NaNNaNNaNNaNNaNNaN接面道路無NaNNaN市街化区域及び市街化調整区域外の都市計画区域70.0200.02007年第４四半期NaN隣地の購入0.32
356343356344宅地(土地と建物)住宅地13421東京都小笠原村母島NaNNaNNaN270ほぼ長方形17.0110平成10年鉄骨造住宅NaN南村道6.0市街化区域及び市街化調整区域外の都市計画区域70.0200.02007年第４四半期NaNNaN21.00